Many signals derived from real world systems exhibit changes over time. Some of the changes may be anomalous behaviors, such as sudden and unexpected changes like spikes or dips in the signal. An anomaly may correspond to a pattern in the signal that deviates from established normal behavior. Other changes may be relatively longer term changes such as a trend change in the signal, referred as regime shifts. It is often desirable to differentiate between the regime shifts and the anomalies in the signal. Traditional single, monolithic algorithms to detect anomalies are challenged to separate a regime shift from an anomaly.
Systems and methods to detect anomalies while accounting for regime shifts would therefore be of great benefit in offline data analysis. It is also important to reduce the false alarms by differentiating between an anomaly and noise.